es.upm.etsisi.cf4j.recommender.matrixFactorization

Class CLiMF

java.lang.Object

es.upm.etsisi.cf4j.recommender.Recommender

es.upm.etsisi.cf4j.recommender.matrixFactorization.CLiMF

public class CLiMF
extends Recommender

Implements Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., & Hanjalic, A. (2012, September). CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems (pp. 139-146).

Field Summary

Fields

Modifier and Type	Field and Description
protected static double	DEFAULT_GAMMA
protected static double	DEFAULT_LAMBDA
protected double	gamma Learning rate
protected double	lambda Regularization
protected int	numFactors Number of latent factors
protected int	numIters Number of iterations
protected double	threshold Threshold to binarize rating matrix.
protected double[][]	U Users' latent factors
protected double[][]	V Items's latent factors

Fields inherited from class es.upm.etsisi.cf4j.recommender.Recommender

datamodel

Constructor Summary

Constructors

Constructor and Description
CLiMF(DataModel datamodel, int numFactors, double gamma, double lambda, int numIters, double threshold) Model constructor
CLiMF(DataModel datamodel, int numFactors, double gamma, double lambda, int numIters, double threshold, long seed) Model constructor
CLiMF(DataModel datamodel, int numFactors, int numIters) Model constructor
CLiMF(DataModel datamodel, int numFactors, int numIters, double threshold) Model constructor
CLiMF(DataModel datamodel, int numFactors, int numIters, double threshold, long seed) Model constructor
CLiMF(DataModel datamodel, int numFactors, int numIters, long seed) Model constructor
CLiMF(DataModel datamodel, Map<String,Object> params) Model constructor from a Map containing the model's hyper-parameters values.

Method Summary

Modifier and Type	Method and Description
void	fit() Estimates model parameters given the hyper-parameters
double	getGamma() Get the learning rate parameter of the model
double	getLambda() Get the regularization parameter of the model
int	getNumFactors() Get the number of factors of the model
int	getNumIters() Get the number of iterations
double	predict(int userIndex, int itemIndex) Computes a rating prediction
String	toString()

Methods inherited from class es.upm.etsisi.cf4j.recommender.Recommender

getDataModel, predict

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

DEFAULT_GAMMA

protected static final double DEFAULT_GAMMA

See Also:

Constant Field Values

DEFAULT_LAMBDA

protected static final double DEFAULT_LAMBDA

See Also:

Constant Field Values

numFactors

protected int numFactors

Number of latent factors

gamma

protected double gamma

Learning rate

lambda

protected double lambda

Regularization

numIters

protected int numIters

Number of iterations

threshold

protected double threshold

Threshold to binarize rating matrix. Any rating greater or equal than this threshold will be used during the training process.

U

protected double[][] U

Users' latent factors

V

protected double[][] V

Items's latent factors

Constructor Detail

CLiMF

public CLiMF(DataModel datamodel,
             Map<String,Object> params)

Model constructor from a Map containing the model's hyper-parameters values. Map object must contains the following keys:

• numFactors: int value with the number of latent factors.
• numIters:: int value with the number of iterations.
• threshold: double value representing the rating value that binaries the matrix.
• gamma (optional): double value with the learning rate hyper-parameter. If missing, it is set to 1E-5.
• lambda (optional): double value with the regularization hyper-parameter. If missing, it is set to 1E-4.
• seed (optional): random seed for random numbers generation. If missing, random value is used.

Parameters:

datamodel - DataModel instance

params - Model's hyper-parameters values

CLiMF

public CLiMF(DataModel datamodel,
             int numFactors,
             int numIters)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

CLiMF

public CLiMF(DataModel datamodel,
             int numFactors,
             int numIters,
             long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

seed - Seed for random numbers generation

CLiMF

public CLiMF(DataModel datamodel,
             int numFactors,
             int numIters,
             double threshold,
             long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

threshold - Threshold to binarize rating matrix. Any rating greater or equal than this threshold will be used during the training process.

seed - Seed for random numbers generation

CLiMF

public CLiMF(DataModel datamodel,
             int numFactors,
             int numIters,
             double threshold)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

numIters - Number of iterations

threshold - Threshold to binarize rating matrix. Any rating greater or equal than this threshold will be used during the training process.

CLiMF

public CLiMF(DataModel datamodel,
             int numFactors,
             double gamma,
             double lambda,
             int numIters,
             double threshold)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

gamma - Learning rate

lambda - Regularization

numIters - Number of iterations

threshold - Threshold to binarize rating matrix. Any rating greater or equal than this threshold will be used during the training process.

CLiMF

public CLiMF(DataModel datamodel,
             int numFactors,
             double gamma,
             double lambda,
             int numIters,
             double threshold,
             long seed)

Model constructor

Parameters:

datamodel - DataModel instance

numFactors - Number of latent factors

gamma - Learning rate

lambda - Regularization

numIters - Number of iterations

threshold - Threshold to binarize rating matrix. Any rating greater or equal than this threshold will be used during the training process.

seed - Seed for random numbers generation

Method Detail

fit

public void fit()

Description copied from class: Recommender

Estimates model parameters given the hyper-parameters

Specified by:

fit in class Recommender

predict

public double predict(int userIndex,
                      int itemIndex)

Description copied from class: Recommender

Computes a rating prediction

Specified by:

predict in class Recommender

Parameters:

userIndex - Index of the user in the array of Users of the DataModel instance

itemIndex - Index of the item in the array of Items of the DataModel instance

Returns:

Prediction

toString

public String toString()

Overrides:

toString in class Object

getNumFactors

public int getNumFactors()

Get the number of factors of the model

Returns:

Number of factors

getNumIters

public int getNumIters()

Get the number of iterations

Returns:

Number of iterations

getLambda

public double getLambda()

Get the regularization parameter of the model

Returns:

Lambda

getGamma

public double getGamma()

Get the learning rate parameter of the model

Returns:

Gamma